// Copyright 2021-present StarRocks, Inc. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
//  Copyright (c) 2011-present, Facebook, Inc.  All rights reserved.
//  This source code is licensed under both the GPLv2 (found in the
//  COPYING file in the root directory) and Apache 2.0 License
//  (found in the LICENSE.Apache file in the root directory).
//
// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.

#pragma once
#include <stdint.h>

#include <algorithm>
#include <random>

namespace starrocks {

// A very simple random number generator.  Not especially good at
// generating truly random bits, but good enough for our needs in this
// package.
class Random {
private:
    enum : uint32_t {
        M = 2147483647L // 2^31-1
    };
    enum : uint64_t {
        A = 16807 // bits 14, 8, 7, 5, 2, 1, 0
    };

    uint32_t seed_;

    static uint32_t GoodSeed(uint32_t s) { return (s & M) != 0 ? (s & M) : 1; }

public:
    // This is the largest value that can be returned from Next()
    enum : uint32_t { kMaxNext = M };

    explicit Random(uint32_t s) : seed_(GoodSeed(s)) {}

    void Reset(uint32_t s) { seed_ = GoodSeed(s); }

    uint32_t Next() {
        // We are computing
        //       seed_ = (seed_ * A) % M,    where M = 2^31-1
        //
        // seed_ must not be zero or M, or else all subsequent computed values
        // will be zero or M respectively.  For all other values, seed_ will end
        // up cycling through every number in [1,M-1]
        uint64_t product = seed_ * A;

        // Compute (product % M) using the fact that ((x << 31) % M) == x.
        seed_ = static_cast<uint32_t>((product >> 31) + (product & M));
        // The first reduction may overflow by 1 bit, so we may need to
        // repeat.  mod == M is not possible; using > allows the faster
        // sign-bit-based test.
        if (seed_ > M) {
            seed_ -= M;
        }
        return seed_;
    }

    uint64_t Next64() { return (uint64_t{Next()} << 32) | Next(); }

    // Returns a uniformly distributed value in the range [0..n-1]
    // REQUIRES: n > 0
    uint32_t Uniform(int n) { return Next() % n; }

    // Randomly returns true ~"1/n" of the time, and false otherwise.
    // REQUIRES: n > 0
    bool OneIn(int n) { return Uniform(n) == 0; }

    // "Optional" one-in-n, where 0 or negative always returns false
    // (may or may not consume a random value)
    bool OneInOpt(int n) { return n > 0 && OneIn(n); }

    // Returns random bool that is true for the given percentage of
    // calls on average. Zero or less is always false and 100 or more
    // is always true (may or may not consume a random value)
    bool PercentTrue(int percentage) { return static_cast<int>(Uniform(100)) < percentage; }

    // Skewed: pick "base" uniformly from range [0,max_log] and then
    // return "base" random bits.  The effect is to pick a number in the
    // range [0,2^max_log-1] with exponential bias towards smaller numbers.
    uint32_t Skewed(int max_log) { return Uniform(1 << Uniform(max_log + 1)); }

    // Returns a random string of length "len"
    std::string RandomString(int len);

    // Generates a random string of len bytes using human-readable characters
    std::string HumanReadableString(int len);

    // Generates a random binary data
    std::string RandomBinaryString(int len);

    // Returns a Random instance for use by the current thread without
    // additional locking
    static Random* GetTLSInstance();
};

// A good 32-bit random number generator based on std::mt19937.
// This exists in part to avoid compiler variance in warning about coercing
// uint_fast32_t from mt19937 to uint32_t.
class Random32 {
private:
    std::mt19937 generator_;

public:
    explicit Random32(uint32_t s) : generator_(s) {}

    // Generates the next random number
    uint32_t Next() { return static_cast<uint32_t>(generator_()); }

    // Returns a uniformly distributed value in the range [0..n-1]
    // REQUIRES: n > 0
    uint32_t Uniform(uint32_t n) {
        return static_cast<uint32_t>(std::uniform_int_distribution<std::mt19937::result_type>(0, n - 1)(generator_));
    }

    // Returns an *almost* uniformly distributed value in the range [0..n-1].
    // Much faster than Uniform().
    // REQUIRES: n > 0
    uint32_t Uniformish(uint32_t n) {
        // fastrange (without the header)
        return static_cast<uint32_t>((uint64_t(generator_()) * uint64_t(n)) >> 32);
    }

    // Randomly returns true ~"1/n" of the time, and false otherwise.
    // REQUIRES: n > 0
    bool OneIn(uint32_t n) { return Uniform(n) == 0; }

    // Skewed: pick "base" uniformly from range [0,max_log] and then
    // return "base" random bits.  The effect is to pick a number in the
    // range [0,2^max_log-1] with exponential bias towards smaller numbers.
    uint32_t Skewed(int max_log) { return Uniform(uint32_t{1} << Uniform(max_log + 1)); }

    // Reset the seed of the generator to the given value
    void Seed(uint32_t new_seed) { generator_.seed(new_seed); }
};

// A good 64-bit random number generator based on std::mt19937_64
class Random64 {
private:
    std::mt19937_64 generator_;

public:
    explicit Random64(uint64_t s) : generator_(s) {}

    // Generates the next random number
    uint64_t Next() { return generator_(); }

    // Returns a uniformly distributed value in the range [0..n-1]
    // REQUIRES: n > 0
    uint64_t Uniform(uint64_t n) { return std::uniform_int_distribution<uint64_t>(0, n - 1)(generator_); }

    // Randomly returns true ~"1/n" of the time, and false otherwise.
    // REQUIRES: n > 0
    bool OneIn(uint64_t n) { return Uniform(n) == 0; }

    // Skewed: pick "base" uniformly from range [0,max_log] and then
    // return "base" random bits.  The effect is to pick a number in the
    // range [0,2^max_log-1] with exponential bias towards smaller numbers.
    uint64_t Skewed(int max_log) { return Uniform(uint64_t(1) << Uniform(max_log + 1)); }
};

// A seeded replacement for removed std::random_shuffle
template <class RandomIt>
void RandomShuffle(RandomIt first, RandomIt last, uint32_t seed) {
    std::mt19937 rng(seed);
    std::shuffle(first, last, rng);
}

// A replacement for removed std::random_shuffle
template <class RandomIt>
void RandomShuffle(RandomIt first, RandomIt last) {
    RandomShuffle(first, last, std::random_device{}());
}

} // namespace starrocks
